Does Pike13 support real-time webhooks?

Yes! Pike13 has a dedicated Webhooks API that sends real-time notifications for events like new clients, invoice changes, and new visits. This MCP server focuses on the Core and Reporting APIs for querying data.

Can I query staff payroll data?

Through the Reporting API, you can query staff hours, class assignments, and pay rates to calculate or audit your payroll.

What payment gateways are supported?

Pike13 typically integrates with Stripe, Evo, and other major gateways to process credit card and ACH payments directly.

Pike13 MCP Server for Pydantic AI 9 tools — connect in under 2 minutes

Name: Pike13
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Pike13 through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Pike13 "
            "(9 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Pike13?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Pike13 MCP Server

Connect your Pike13 studio to any AI agent and manage your fitness business through natural conversation.

Pydantic AI validates every Pike13 tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Events — View scheduled classes and sessions with instructors, capacity, and enrollment
Clients — Search members by name, email, or phone with detailed profiles
Staff — List instructors with roles, certifications, and class assignments
Plans — Browse membership plans, class packs, and drop-in pricing
Invoices — Track revenue, payments, refunds, and financial reporting
Visits — Analyze check-in records for retention and engagement analytics
Business — Access account profile and configuration

Why Pike13?

Pike13 uniquely offers a Reporting API with deep analytics — making it the best choice for data-driven studio owners who want AI-powered business intelligence. Combined with the Core API and Webhooks, it provides the most comprehensive data access in the fitness industry.

The Pike13 MCP Server exposes 9 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Pike13 to Pydantic AI via MCP

Follow these steps to integrate the Pike13 MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 9 tools from Pike13 with type-safe schemas

Why Use Pydantic AI with the Pike13 MCP Server

Pydantic AI provides unique advantages when paired with Pike13 through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Pike13 integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Pike13 connection logic from agent behavior for testable, maintainable code

Pike13 + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Pike13 MCP Server delivers measurable value.

Type-safe data pipelines: query Pike13 with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Pike13 tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Pike13 and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Pike13 responses and write comprehensive agent tests

Pike13 MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect Pike13 to Pydantic AI via MCP:

get_business_info

Get business account info

get_event

Get event details

get_person

Get person profile

list_events

Filter by date range. Follows JSON:API 1.0 spec. List scheduled classes/events

list_invoices

Filter by date for financial reporting. List invoices and revenue

list_plans

Includes pricing and visit limits. List service plans

list_staff

List staff members

list_visits

Key data for retention analytics. List member visits

search_people

Returns profiles, active plans, visit count, and account balance. Search clients/members

Example Prompts for Pike13 in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Pike13 immediately.

"What's our revenue this month?"

"List the staff members teaching yoga classes tomorrow."

"How many new clients bought a membership this week?"

Troubleshooting Pike13 MCP Server with Pydantic AI

Common issues when connecting Pike13 to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Pike13 + Pydantic AI FAQ

Common questions about integrating Pike13 MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Pike13 MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.